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--- mobility_data:top [2022/06/09 04:31] – [Meeting Note] zhiyuanpeng
+++ mobility_data:top [2022/10/24 09:24] (current) – [Meeting Note] zhiyuanpeng
@@ Line 1: / Line 1: @@
-====== 2021 Project Page ======
+====== 2021-2022 Project Page ======
 ===== Mobility Data Project =====
 ==== Meeting Note ====
+/10/24
+  * Attendee: Zhiyuan, Xiangyu, Yuanbo, Yang
+  * Meeting Summary
+  Xiangyu
+  * Xiangyu shared a instance normalization method for time-series forecasting against distribution shift which published in ICLR 2022.
+    * Using this method the MSE of MLP for gaode's dataset(350days training data,50days test data) improved from 4.65 to 3.26( 30%+).
+    * Next step I will implement this method in our meta-learning framewrok to see the improvements and compare the effiectiveness of our method and the normalization.[Presentation-slides:https://cloud.tsinghua.edu.cn/f/75320708a08f404e8a0b/]
+* Zhiyuan
+  * Summary:
+    * this week, I conducted a series of experiments to compare our Soft-restricted MF Multi-task learning model performance with single loss trained ones.
+    * This weeks experiments reveal that the multitask loss only contributes limitedly to the improvement on the both two tasks.
+    * Moreover, the LSTM based backbone tends to predict more smoother compared to the more fluctuated data in reality.
+    * An important observation: MSE mainly comes from several regions that has great volatility.
+  * Future Plan:
+    * Maybe next week we can try to use multi-source input data or time sequence analysis method to deal with it.
+/6/30
+  * Attendee: Zhiyuan, Xiangyu, Yang
+  * Meeting Summary:
+     * Transcribe the matrixed-version formulation into its Lagrange version
+     * Use gradient descend and integer projection to iteratively update the optimization problem
+     * Interpretation and analysis the loss result of the proposed method
+     * Regard the greedy algorithm as the supremum while the dynamic programming and real-relaxed optimization as the infimum and compute their approximation rate to measure the outcome with the optimal result
+     * Integer rounding the result in real-domain by the hyper parameter threshold.
+     * Make sure the application of the Pathlet is use a top-k dictionary to recomposite new trajectory and find how much information can be kept
+  * TO-DO:
+     * Find the optimal threshold to integer round the real-domain solution, meaning we use matrix formulation to update result while guaranteeing the integer constraints
+     * Find the application for the Pathlet in real-world project such as new coding for the spatial data
 /06/08
   * Attendee: Zhiyuan, Yuanbo, Yang